LLMs + Security = Trouble

• URL: http://arxiv.org/abs/2602.08422v1
• Date: Mon, 09 Feb 2026 09:27:28 GMT
• Title: LLMs + Security = Trouble
• Authors: Benjamin Livshits,
• Abstract summary: "Fighting fire with fire" approach fails to address the long tail of security bugs.<n>We argue that stronger security guarantees can be obtained by enforcing security constraints during code generation.
• Score: 5.235480194772795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We argue that when it comes to producing secure code with AI, the prevailing "fighting fire with fire" approach -- using probabilistic AI-based checkers or attackers to secure probabilistically generated code -- fails to address the long tail of security bugs. As a result, systems may remain exposed to zero-day vulnerabilities that can be discovered by better-resourced or more persistent adversaries. While neurosymbolic approaches that combine LLMs with formal methods are attractive in principle, we argue that they are difficult to reconcile with the "vibe coding" workflow common in LLM-assisted development: unless the end-to-end verification pipeline is fully automated, developers are repeatedly asked to validate specifications, resolve ambiguities, and adjudicate failures, making the human-in-the-loop a likely point of weakness, compromising secure-by-construction guarantees. In this paper we argue that stronger security guarantees can be obtained by enforcing security constraints during code generation (e.g., via constrained decoding), rather than relying solely on post-hoc detection and repair. This direction is particularly promising for diffusion-style code models, whose approach provides a natural elegant opportunity for modular, hierarchical security enforcement, allowing us to combine lower-latency generation techniques with generating secure-by-construction code.

Related papers

• Inference-Time Safety For Code LLMs Via Retrieval-Augmented Revision [3.983997834693767]
  Large Language Models (LLMs) are increasingly deployed for code generation in high-stakes software development.<n>LLMs cannot readily adapt to newly discovered vulnerabilities or changing security standards without retraining.<n>We present a principled approach to trustworthy code generation by design that operates as an inference-time safety mechanism.
  arXiv  Detail & Related papers  (2026-03-02T06:06:34Z)
• Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model [60.60587869092729]
  Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to real-world deployment.<n>We propose SecCoderX, an online reinforcement learning framework for functionality-preserving secure code generation.
  arXiv  Detail & Related papers  (2026-02-07T07:42:07Z)
• RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
  arXiv  Detail & Related papers  (2026-01-30T08:29:01Z)
• Towards Verifiably Safe Tool Use for LLM Agents [53.55621104327779]
  Large language model (LLM)-based AI agents extend capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents.<n>LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records.<n>Current approaches to mitigate these risks, such as model-based safeguards, enhance agents' reliability but cannot guarantee system safety.
  arXiv  Detail & Related papers  (2026-01-12T21:31:38Z)
• STELP: Secure Transpilation and Execution of LLM-Generated Programs [2.986494009382113]
  Large Language Models (LLMs) can solve software development-related tasks such as code generation.<n>LLMs generated code could be unstable or erroneous and contain vulnerabilities that could lead to widespread system malfunctions.<n>This paper proposes a Secure Transpiler and Executor of LLM-Generated Program (STELP) capable of executing LLM-generated code in a controlled and safe manner.
  arXiv  Detail & Related papers  (2026-01-09T01:49:41Z)
• TypePilot: Leveraging the Scala Type System for Secure LLM-generated Code [46.747768845221735]
  Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages.<n>Their outputs often contain subtle but critical vulnerabilities, posing significant risks when deployed in security-sensitive or mission-critical systems.<n>This paper introduces TypePilot, an agentic AI framework designed to enhance the security and robustness of LLM-generated code.
  arXiv  Detail & Related papers  (2025-10-13T08:44:01Z)
• Secure Tug-of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security [63.41350337821108]
  We propose Secure Tug-of-War (SecTOW) to enhance the security of multimodal large language models (MLLMs)<n>SecTOW consists of two modules: a defender and an auxiliary attacker, both trained iteratively using reinforcement learning (GRPO)<n>We show that SecTOW significantly improves security while preserving general performance.
  arXiv  Detail & Related papers  (2025-07-29T17:39:48Z)
• Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation [16.29310628754089]
  Large Language Models (LLMs) have become powerful tools for automated code generation.<n>LLMs often overlook critical security practices, which can result in the generation of insecure code.<n>This paper examines their inherent tendencies to produce insecure code, their capability to generate secure code when guided by self-generated vulnerability hints, and their effectiveness in repairing vulnerabilities when provided with different levels of feedback.
  arXiv  Detail & Related papers  (2025-06-28T23:24:33Z)
• Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics [5.384257830522198]
  Large Language Models (LLMs) in critical applications have introduced severe reliability and security risks.<n>These vulnerabilities have been weaponized by malicious actors, leading to unauthorized access, widespread misinformation, and compromised system integrity.<n>We introduce a novel approach to detecting abnormal behaviors in LLMs via hidden state forensics.
  arXiv  Detail & Related papers  (2025-04-01T05:58:14Z)
• CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
  This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs. Our studies reveal a new and universal safety vulnerability of these models against code input. We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
  arXiv  Detail & Related papers  (2024-03-12T17:55:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.